Zishang Jiang

LLMs struggle to reliably apply skills, with performance varying significantly based on the agent harness used, challenging assumptions about their capability.

ADaPT enables a single model to flexibly navigate the efficiency-performance trade-off, achieving significant cost savings without sacrificing reasoning quality.

LLM agent performance hinges on maximizing decision-relevant information density within context, not just context length, and GenericAgent proves it.